Datasets and metadata supporting the article: Genomic investigation of co-targeting tumor immune microenvironment and immune checkpoints in pan-cancer immunotherapy

Drugs that target immune checkpoints (ICPs) have become the most popular weapons in cancer immunotherapy; however, they are only beneficial for a small fraction of patients. Accumulating evidence suggests that the tumor immune microenvironment (TIME) plays a critical role in anti-cancer immunity. Therefore, this study aimed to assess the potential merits and feasibility of combinational targeting ICPs and TIME in cancer immunotherapy.Data access: This pan-cancer analysis study used publicly available datasets. The list of publicly available datasets used is provided in the file “Links to all publicly available datasets_.docx”. The analysis data files used to generate the figures in the published article, are publicly available in the figshare repository as part of this data record.Study approval and patient consent: Not applicable, because this study only analyzed open access human databases, and did not involve the use of any human research participants or human tissue.Study aims and methodology: The capability of the immune system to recognize and eradicate malignant cells was already identified several decades ago, and dysfunction of immune system was deemed as one of the hallmarks of cancer development. Cancer immunotherapies were developed to reactivate the anti-tumor immune response, thus helping to recognize and eliminate tumors.To further improve the therapeutic precision and limit the side-effects of immune checkpoint blockade (ICB)-based therapies, this study combined The Cancer Genome Atlas (TCGA) and several other open access genomic databases to analyze the defects of ICB treatment. The study examined the targeting of tumor immune microenvironment (TIME) factors in anti-cancer immunotherapy, and a combination strategy of co-targeting ICPs and TIME factors was further discussed to increase tumor immunogenicity, favor intra-tumoral T-cell infiltration, and enhance ICB efficacy.A total of 31 cancer type-specific datasets in TCGA were individually collected by the publicly available web servers for multiple bioinformatic analyses of ICPs and TIME factors. Briefly, Gene Expression Profiling Interactive Analysis (GEPIA) was used to calculate the prognostic indexes, Search Tool for Recurring Instances of Neighbouring Genes (STRING) was used to construct protein-protein interactions, cBioPortal was used for visualization and comparison of genetic alterations, and Tumor and Immune System Interaction Database (TISIDB) was used to explore the correlation to tumor-infiltrating lymphocytes (TILs).For more details on the methodology, please read the related article.Datasets supporting the figures and tables in the published article: The following folders are publicly available as part of this data record:Data supporting figure 1: ICPs expression profile.zip of .svg filesData supporting figure 2: ICPs survival analyses.zip of .pdf filesData supporting figure 3: TIME factors expression profile.zip of .svg filesData supporting figure 4: TIME factors survival analyses.zip of .pdf filesData supporting figure 5: ICPs-TIME factors interaction network.zip of .png, .tsv, .xml and .txt filesData supporting figure 6: ICPs-TIME factors genetic alterations.zip of .pdf, .png and .svg filesData supporting figure 7: ICPs-TIME factors survival analyses.zip of .pdf, .png, .svg and .txt filesData supporting figure 8: ICPs-TIME factors-ICD mediators-effector T correlation.zip of .pdf filesData supporting figure 9: ICD mediators-TILs correlations.zip of .png files.Data supporting supplementary table 1: ICPs-TIME factors co-occurrence table.tsvThe data file Links to all publicly available datasets_.docx contains a list of all the publicly available datasets used in this study.Software needed to access datasets: The The SVG files can be accessed using Adobe Illustrator.